# Second Pass Training of the ViDiCheck Tool

1. With all of the labels applied in each view, press the Brain icon in the Tool Control to re-train the tool.

2. Again, we will need to review the results in the Database Overview and re-examine the Views to verify that the tool correctly applied markings to the labels.

3. We will need to follow the same steps we used during the bootstrap labeling process to correct mislabeled features.

4. In addition, we can manually select certain potentially problematic views and add them directly to the training set so that they will be used during training. To do so, right-click within the Image Display Area and select Add to training set from the menu.

5. To also improve the trained neural network, we will utilize the Perturbation Tool Parameters to enhance our training set. In this example, due to the circular nature of the feature, the Rotation parameter is set to [-180°, 180°], and the Luminance and Contrast parameters are set to 5% to help account for the variations in the lighting of the features.

   Note: Any change to the Perturbation parameters will necessitate that the tool be retrained.

6. In addition to the changes to the Perturbation parameters, the Selection parameter of the Select Training Set dialog was increased from 50% to 70% to increase the percentage of labeled images to use in training the neural network.

7. After training, using the Database Overview once again, we will review the results. This time, we received much better results, with only two instances of the Milk labeled feature not being found (and being misidentified as Dark).

8. By clicking the small icon in the Confusion Matrix, the Display filter will automatically be configured to display the two mismatched instances of the features. For the two instances, repeat the steps to accept the view and correctly label the image. In both these instances, since the images weren't used during training, you can also manually add them to the training set.

9. Train the tool again and review the results. The results indicate a well constructed neural network model.